Gene expression analysis provides the foundation for studying thousands of individual alterations in gene function. These alterations in mRNA expression can be viewed as biomarkers. Whole genome gene expression assays are routinely used to predict treatment responses in human diseases (Xiang et al. Curr Opin Drug Discov Devel. 2003; 6:384-95; and Lee J. S. and Thorgeirsso, S. S. Gastroenterology 2004; 127:S51-55). A major limitation with the gene expression data analysis methods is the low prediction accuracy with small sample size (Roepman P. Bioanalysis 2010; 2:249-62). Studies have indicated that the prediction accuracy can be increased by increasing the sample size. For example, Ein-Dor et al. reported that ˜3000 samples are needed to get good prediction accuracy necessary for the clinical applications in lung cancer (Proc Natl Acad Sci USA 2006; 103:5923-5928). It has also been proposed that gene expression data can be supplemented with copy number variation and Single Nucleotide Polymorphism (SNP) information to obtain the accuracy required for class prediction (Kalia M. Metabolism 2013; 62:S11-14). Using more than one technique, however, will increase the cost of the test and also the complexity associated with the present data analysis methods.
Currently, a supervised clustering method is used to analyze microarray data to classify a patient for treatment response (Speed, T. (Ed.) 2003 Statistical analysis of gene expression microarray data. Chapman and Hall/CRC, NewYork).
The goal of these approaches is to relate gene expression to different target classes and to use this new information to produce a prediction model. Often, this approach is called pattern recognition. There are many different algorithms, such as linear predictors, neural nets, etc. These are very powerful tools, but each has its own advantages and disadvantages. One would need to know how to select the right method, structure, and definition for a given problem. This approach may not provide accurate results to take clinical decision. For example, a prediction model developed by Gordon et al. could reach only 74% predicting accuracy with ˜400 samples; a good outcome but not excellent result (Gordon et al. Can Epidemiol Biomarkers Prev 2003; 12: 905-910).
Musical algorithms have been widely used to compose tunes for entertainment purposes. There is a limited usage in medical musical therapy applications (Carr et al. PLoS One 2013; 8:e70252).